1. Fe–Al–Si Thermoelectric (FAST) Materials and Modules: Diffusion Couple and Machine-Learning-Assisted Materials Development
- Author
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Zhufeng Hou, Teruyuki Ikeda, Hiroyasu Kojima, Koji Tsuda, and Yoshiki Takagiwa
- Subjects
Fabrication ,Materials science ,Dopant ,business.industry ,02 engineering and technology ,Power factor ,021001 nanoscience & nanotechnology ,Machine learning ,computer.software_genre ,Thermoelectric materials ,01 natural sciences ,Power (physics) ,Thermoelectric generator ,Electricity generation ,0103 physical sciences ,Thermoelectric effect ,General Materials Science ,Artificial intelligence ,010306 general physics ,0210 nano-technology ,business ,computer - Abstract
To lower the introduction and maintenance costs of autonomous power supplies for driving Internet-of-things (IoT) devices, we have developed low-cost Fe-Al-Si-based thermoelectric (FAST) materials and power generation modules. Our development approach combines computational science, experiments, mapping measurements, and machine learning (ML). FAST materials have a good balance of mechanical properties and excellent chemical stability, superior to that of conventional Bi-Te-based materials. However, it remains challenging to enhance the power factor (PF) and lower the thermal conductivity of FAST materials to develop reliable power generation devices. This forum paper describes the current status of materials development based on experiments and ML with limited data, together with power generation module fabrication related to FAST materials with a view to commercialization. Combining bulk combinatorial methods with diffusion couple and mapping measurements could accelerate the search to enhance PF for FAST materials. We report that ML prediction is a powerful tool for finding unexpected off-stoichiometric compositions of the Fe-Al-Si system and dopant concentrations of a fourth element to enhance the PF, i.e., Co substitution for Fe atoms in FAST materials.
- Published
- 2021
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